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南海城乡结合地区居民高血压患病率及其影响因素分析
引用本文:何旭研,曹钰芹,林佩卿. 南海城乡结合地区居民高血压患病率及其影响因素分析[J]. 中国慢性病预防与控制, 2011, 19(5): 477-479
作者姓名:何旭研  曹钰芹  林佩卿
作者单位:广东省佛山市南海区第二人民医院,528251
摘    要:目的了解南海城乡结合地区高血压患病率,分析其患病的影响因素。方法采用分层随机抽样的方法,由经过培训合格的调查员进行面对面问卷调查和体格检查。结果实际调查18岁以上常住居民8168人,高血压患者为1542人,患病率为18.88%,标化率为12.44%,其中男性为13.62%,女性为11.43%,男女患病率之间差异具有统计学意义(P<0.05)。高血压患病率随年龄增加而升高(P〈0.05)。年龄(OR=1.623~21.332)、吸烟(OR=1.350)、BMI(OR=1.623~6.083)和糖尿病(OR=3.155)是高血压的独立危险因素,文化程度(OR=0.720~0.358)是保护因素。结论该地区高血压患病率较高,年龄、文化程度、吸烟、BMI和糖尿病是影响该地区高血压患病的主要因素。

关 键 词:高血压  影响因素  城乡结合地区  Logistic回归

The Prevalent Rate of Hypertension and its Influencing Factors in Urban-rural Fringe Areas of Nanhai
HE Xu-yan , CAO Yu-qing , LIN Pei-qing. The Prevalent Rate of Hypertension and its Influencing Factors in Urban-rural Fringe Areas of Nanhai[J]. Chinese Journal of Prevention and Control of Chronic Non-Communicable Diseases, 2011, 19(5): 477-479
Authors:HE Xu-yan    CAO Yu-qing    LIN Pei-qing
Affiliation:. (The Second People's Hospital of Nanhai District in Foshan,Foshan 528251, China)
Abstract:Objective To understand the prevalent rate of hypertension, and analyze its influencing factors. Methods Using stratified random sampling method, by trained and qualified investigators to investigate the home face to face. Results Of 8168 subjects, 1542 were found to be with hypertension, the prevalent rate was 18.88% and the standardized rate was 12.44%. male and female accounted for13.62.%, 11.43%, the prevalent rate of hypertension was significant difference between men and women (P〈0.05). The prevalence increased with age (P〈0.05).Age(OR =1.623-21.332),smoking(OR =1.350),BMI(OR =1.623-6.083)and diabetes (OR =3.155) were independent risk factors of hypertension. Educational level (OR =0.720-0.358) was independent protective factor. Conclusion The prevalence rate of hypertension in this region was high, age, educational level, smoking, BMI, diabetes were main factors of hypertension, and should be carried out for its focus on its prevention and control factors.
Keywords:Hypertension  Influence factor  Urban-rural fringe area  Logistic regression
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